Perovskite quantum dots (PQDs) are promising nanomaterials for optoelectronic and energy-conversion applications due to their high photoluminescence quantum yield, tunable bandgap, and defect tolerance. However, their soft ionic lattices exhibit dynamic disorder, anharmonic vibrations, and ion migration, leading to challenges in structural and optoelectronic stability. This review elucidates how molecular dynamics (MD) simulations decode the interplay of atomic fluctuations, lattice dynamics, and surface reconstruction in PQDs, revealing mechanisms governing their stability and performance. It highlights how multi-scale MD frameworks link femtosecond-scale atomic motions to microsecond-scale degradation processes, enabling predictive design of resilient PQDs. Key findings include the role of anharmonic lattice vibrations in defect tolerance, the impact of surface ligand dynamics on emission linewidth, and the mitigation of ion migration through matrix embedding, offering strategies for enhanced device reliability. Classical molecular dynamics (CMD) simulations employ Lennard-Jones, Buckingham, ReaxFF, and COMB3 force fields to model mesoscale phenomena, including ligand rearrangement and surface fusion. Ab initio molecular dynamics (AIMD) uses density functional theory (DFT) with PBE and HSE06 functionals, typically with plane-wave basis sets (e.g., PAW), to capture ion-electron coupling and lattice anharmonicity. Nonadiabatic molecular dynamics (NAMD) incorporates surface hopping to simulate ultrafast charge transfer. Machine-learning potentials, trained via neural networks and Gaussian processes, enhance sampling with metadynamics and replica-exchange techniques.

CuInTe2 is a promising semiconductor with a tunable bandgap of 1.0-1.2 eV, enabling it to efficiently absorb sunlight and convert it into usable energy. Following this development, characterization of its structural and electronic properties is currently underway. In this study, the Vienna Ab Initio Simulation Package (VASP) with density functional theory (DFT) and plane-wave basis sets was used to investigate the structural and electronic properties of both neutral and anionic clusters. For (CuIn)nTe2 and ((CuIn)nTe2)− (n = 1–8) clusters, geometric optimization revealed the lowest-energy isomers, all of which adopt cubic chalcopyrite structures. According to the results, the low-lying energy geometry of Cu2In2Te2 and (CuInTe2)− clusters exhibit their maximum relative stability. The (CuIn)nTe2 thin-film experimental finding of 1.85 eV is a good match with their mean HOMO–LUMO gaps of 1.652 eV and 2.464 eV. Binding energy per atom increases with cluster size, although the HOMO–LUMO gap breaks at n = 5, most likely as a result of bond-specific interactions and orbital hybridization. The Cu2In2Te2 cluster stands out with maximum HOMO–LUMO gap and dissociation energy, consistent with its enhanced stability. Adiabatic ionization potentials decrease with cluster size, indicating growing metallic character, while dissociation energies show odd–even oscillations but overall increase as size grows. Partial charge density analysis shows that both neutral and anion clusters are significant for semiconductor applications, including photovoltaic cells and related devices.

The reliability of the random phase approximation (RPA) and of σ-functional methods in conjunction with the mixed Gaussian and plane wave (GPW) basis set scheme as implemented in the CP2K package is investigated. First, based on the results for thermochemical properties of molecules and structural properties of crystalline solids, we establish reliable computational setups for practical calculations. Next, we compare the results obtained with these setups to those from standard GPW basis set approaches. For molecules, the results of RPA and σ-functional calculations within the GPW scheme are slightly worse, though still comparable, to those obtained using the standard Gaussian basis set scheme, provided a large enough orbital basis set is employed in the GPW calculations. Furthermore, the GPW calculations using σ-functionals are clearly more accurate than RPA calculations and even more so than those of conventional Kohn-Sham methods in the prediction of reaction energies and barrier heights for main group chemistry. For crystalline solids, the RPA and σ-functional methods significantly outperform the conventional Perdew-Burke-Ernzerhof (PBE) method in determining lattice constants. However, only the RPA method provides improved results for bulk moduli, while the σ-functional method yields errors comparable to those of the PBE method. A comparison of the results of the plane wave basis set calculations with the projector augmented wave method shows reasonable consistency for lattice constants and bulk moduli.

Quantum many-body calculations are fundamentally limited by the exponential growth of the configuration space, making accurate treatment of strongly correlated systems computationally prohibitive. Here we present the Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI), a Transformer-based machine learning approach that generatively samples important configurations to accelerate many-body quantum chemistry calculations. By leveraging the Transformer architecture's self-attention mechanism to capture long-range electron correlations, GTNN-SCI achieves up to 10× speedup compared to state-of-the-art neural network methods while maintaining high accuracy. We demonstrate the efficacy of GTNN-SCI by calculating correlation and binding energies for representative molecules including N2, H2O, and C2 using both Gaussian (cc-pVDZ) and plane-wave basis sets, achieving faster convergence and lower energies than previously reported neural network-based selected CI techniques. Most significantly, our generative approach identifies higher-order excitations missed by conventional coupling schemes, yielding lower variational energies than established methods including heat-bath CI. This capability enables GTNN-SCI to accurately treat the strongly correlated [Fe2S2(SCH3)4]2- ([2Fe-2S]) cluster system, achieving ground-state energies within chemical accuracy of DMRG benchmarks, whereas conventional selected CI methods have failed on this system. The GTNN-SCI method thus combines modern deep learning with high-performance electronic structure computation, providing an efficient and precise avenue for solving the electronic Schrödinger equation in challenging molecular systems.

ABACUS (Atomic-orbital Based Ab initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as deep potential with attention models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several artificial intelligence-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

This work investigates the initial stages of the reaction in the conversion of chloromethane to light olefins (CMTO) on NaZSM-5 zeolite. Density functional theory (DFT) calculations indicate that the overall reaction is endothermic in bulk but exothermic on the surface. The formation of the :CH2 species is highly endothermic and has a high activation barrier. The migration of the CH3 group between oxygen atoms within the zeolite structure is energetically less demanding than the formation of the :CH2 species. Finally, the formation of ZOCH2CH3 from ZOCH3 + CH3Cl occurs through a two-step mechanism that includes a migration of the CH3 group with energy barriers of +95.2 and +148.2kJmol-1. The results obtained with ONIOM2 are qualitatively comparable to those obtained with VASP in terms of energy and geometry. DFT calculations were carried out using Gaussian 09 and VASP code. For cluster models, ONIOM2(DF:PM3) was used with the ωB97x-D, PBE, and M062X density functionals (DF) along with the relativistic Stevens effective core potential and its corresponding basis set. QST2, IRC, and frequency calculations are performed to characterize the transition states. For periodic calculations, structure optimization was carried out using the PBE functional with the D3 dispersion of Grimme, a plane-wave basis set, and the PAW representation of the atomic cores. The electronic structures of the systems were analyzed using PDOS.

In this work, the structural, electronic, and thermodynamicstabilitiesin the novel two-dimensional monolayer (2D-ML) structure of IIB–VAzinc pnictides, ZnX (X = As, Sb, Bi), have been systematically investigatedvia lattice engineering. We utilize the geometries of 3D bulk structuresof ZnX in orthorhombic symmetry with space group Pbca(No.61) as parental material to model three different2D monolayers of ZnX, denoted as 2D-(L1, L2, and L3). Their totalrelative energies and stabilities have been investigated and comparedwith the 2D monolayer geometries of tetragonal, hexagonal (planarhoneycomb), and wurtzite (puckered honeycomb) symmetries. The spin-polarizeddensity functional theory (DFT) with plane wave basis sets has beenemployed throughout the calculations, with the hybrid HSE06 functionalto get an accurate description of thermodynamic stability and electronicband gap values consistent with experimental data. Our calculationssuggest that the 2D-L1 monolayer with rectangular symmetry obtainedfrom the lattice relaxation of quasi-layered rhomboid rings (Zn2X2) dramatically represents the ground-state monolayerin zinc pnictide compounds. While the 2D-ML in tetragonal geometryis energetically competitive in ZnSb or favorable in ZnBi, it showsslight dynamical instability, reinforcing that 2D-L1 is the only structurefound to be dynamically stable at zero strain. The feasibility ofthe most stable 2D-L1 monolayer has been realized with its dynamicalstability, as manifested by the absence of imaginary frequencies inphonon dispersion curves, together with mechanical and thermal stabilitiesvia ab initio molecular dynamics (AIMD). The bandgap becomes wider in the 2D-L1 monolayer compared to its bulk counterparts.The nature of the band gap is slightly indirect in 2D-L1 monolayerof ZnAs, whereas it is direct in that of ZnSb, and ZnBi. Notably,the indication of a negative Poisson’s ratio in the most stable2D-L1 monolayer in ZnAs signifies its auxetic property. Our theoreticalfindings provide a potential synthesis route for novel 2D-ML structuresin ZnX, which have yet to be experimentally synthesized.

Spin-dependent linear-response time-dependent density functional theory has emerged as a powerful computational tool for accurately describing electronic excitations in complex spin-dependent systems. In this work, we introduce an efficient numerical implementation integrated into our open-source software KSSOLV (Kohn–Sham Solver) with plane wave basis sets, which systematically treats both spin-conserving and spin–flip excitation processes. Through comprehensive benchmark calculations, we validate both the numerical accuracy and computational efficiency of our implementation using representative molecular and periodic systems. In particular, the benchmarks encompass closed-shell molecules, such as nitrogen (N2), water (H2O), and carbon monoxide (CO), and the open-shell molecule oxygen (O2), along with periodic examples, including spin-unpolarized silicon (Si64) and the negatively charged nitrogen-vacancy center (NV−) in diamond. Comparative analyses with established quantum chemistry and materials science codes utilizing the Gaussian-type orbitals and plane wave basis sets confirm its robustness and capability to address previously challenging spin-dependent periodic systems.

The second-order Møller-Plesset perturbation (MP2) theory is a post-Hartree-Fock method widely used to describe weak correlation energies in solids and molecules, but its high computational cost scales as O(N5). Herein, we present an accurate and efficient implementation of MP2 within the plane-wave (PW) basis set for both periodic and molecular systems, which incorporates the interpolative separable density fitting (ISDF) decomposition and the Laplace transformation (LT) of the energy denominator. These innovations avoid the direct construction of electron repulsion integrals (ERIs) and reduce the computational complexity of MP2 from O(N5) to O(N4). The key idea for reducing the scaling is to exploit the numerical redundancy of occupied-virtual molecular orbital pairs on the real-space grid in the plane-wave basis set, which enables ERIs to be factorized into lower-rank quantities. This leads to further cost reductions in both the direct and exchange terms of the MP2 correlation energy. For a bulk silicon system consisting of 128 atoms, the LT-ISDF-MP2 method demonstrates a 13.5-fold speedup in total computation time compared to the standard approach. Using this plane-wave LT-ISDF-MP2 method, we simulate the π-π stacking interaction in the 1,3-butadiene dimer, successfully capturing the dispersion interaction and reproducing the self-assembled configuration.

Quantum simulation of materials is a promising application area of quantum computers. To practically realize this promise, we must reduce quantum resources while maintaining accuracy. In electronic structure calculations on classical computers, resource reduction has been achieved by using the projector augmented-wave method (PAW) and plane wave basis sets. However, the PAW method generalized for many-body states introduces nonorthogonality effects which impede its direct application to quantum computing. In this work, we develop a unitary variant of the PAW (UPAW) that preserves the orthogonality constraints. We provide a linear-combination-of-unitaries decomposition of the UPAW Hamiltonian to enable ground state estimation using qubitized quantum phase estimation. Additionally, we further improve algorithmic efficiency by extending classical down-sampling techniques into the quantum setting. We then estimate quantum resources for crystalline solids to estimate the energy within chemical accuracy with respect to the full basis set limit, and also consider a supercell approach which is more suitable for calculations of defect states. We provide the quantum resources for energy estimation of a nitrogen-vacancy defect center in diamond which is a challenging system for classical algorithms and a quintessential problem in the studies of quantum point defects.

This study investigates the electronic structure and optical properties of Sn-C co-doped β-Ga2O3 at different concentrations using the generalized gradient approximation (GGA + U) method within density functional theory (DFT). The results show that, compared to intrinsic β-Ga2O3, all doped systems induce lattice distortion. Among them, the Sn-C system exhibits higher stability in both oxygen-rich and gallium-rich environments. Additionally, doping significantly reduces the band gap, with the Sn-2C doped system having the smallest band gap (0.98eV), while both the 5 at% system and Sn-3C system display weak metallic characteristics. The static dielectric constant of the co-doped system increases with concentration, enhancing its polarization ability. The absorption spectrum shows clear redshift, with significantly improved absorption in the 150-400nm wavelength range and a trend toward extension into the visible light region. These results suggest that Sn-C co-doping is an effective strategy for optimizing the optoelectronic properties of β-Ga2O3, potentially enhancing its application in optoelectronic devices. In the first-principles calculations, density functional theory (DFT) was employed, using the Perdew-Burke-Ernzerhof (PBE) functional within the generalized gradient approximation (GGA). The calculations were performed using the Cambridge Sequential Total Energy Package (CASTEP) program, where the interaction between valence electrons and ionic cores was treated with on-the-fly generated (OTFG) ultrasoft pseudopotentials. A plane-wave basis set was constructed with a cutoff energy of 450eV. The Brillouin zone was sampled using a 1 × 4 × 2k-point mesh generated by the Monkhorst-Pack method, and structural optimization was carried out using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. During optimization, the following energy convergence criteria were set: a total energy convergence threshold of 10-5eV/atom, a maximum internal stress of 0.05 GPa, an interatomic force less than 0.03eV/nm, and a maximum atomic displacement limited to 10-3Å. The valence electron configurations used in the calculations were Ga (3d10 4s2 4p1), O (2s2 2p4), Sn (5s2 5p2), and C (2s2 2p2). It should be noted that the standard GGA method neglects the strong correlation effects of Ga 3d electrons, which leads to an underestimated band gap compared to experimental values, thereby affecting the accurate assessment of material properties. To address this issue, the GGA + U approach was adopted in this work, introducing Hubbard U corrections to more accurately describe the electronic structure of β-Ga2O3. Specifically, a U value of 6.5eV was applied to the O 2p electrons, and a U value of 10.5eV was applied to the Ga 3d electrons.

The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known materials for non-volatile phase-change memory and related technologies. Using a training set of ab initio electronic energy bands and rotation-covariant descriptors for various GeSe and GST compounds, we generate transferable EPs for Ge, Se, Sb, and Te. We demonstrate that the new ML model accurately reproduces the energy bands and wavefunctions of structures outside the training set, closely matching first-principle calculations. This accuracy is achieved with significantly lower computational costs due to the elimination of self-consistency iterations and the reduced size of the plane-wave basis set. Notably, the method maintains accuracy even for diverse local atomic environments, such as amorphous phases or larger systems not explicitly included in the training set.

Solid solution hardening and softening effects in aluminum alloys by substitutional foreign atoms are studied using quantum‐mechanical first‐principles modeling of periodically repeated supercells, with full density functional theory (DFT) calculations in the generalized gradient approximation, employing a plane wave basis set and pseudopotentials. In supercell models of the Shockley partial dislocation, the electronic and steric interactions of the alloying atoms are investigated within the dislocation core and the surrounding Cottrell atmosphere. The DFT‐derived energy barrier toward dislocation motion is most strongly modified for substitutional sites located close to the dislocation core. The influence of the different substituents increases with the propensity to form binary aluminum compounds, but with an element‐specific variation: Si lowers the barrier, indicating a potential solid solution softening effect, whereas Zr increases the barrier, which correlates with an impeded dislocation motion. On the quantum scale, Mg is of little influence with a slight tendency toward a barrier increase, although its classical alloying parameters are identical to those of Zr. Thus, calculating from first principles the additional electronic contributions to the still commonly applied classical size‐stress theory is indispensable for alloying near dislocation cores. This indicates that electronic interactions need to be included for refining the classical model.

The vibrational frequency of carbon monoxide (CO) adsorbed on ceria-based catalysts serves as a sensitive probe for identifying exposed surface facets, provided that experimental reference data on well-defined single-crystal surfaces and reliable theoretical assignments are available. Previous studies have shown that the hybrid density functional theory approach using the HSE06 functional yields good agreement with experimental observations, whereas the generalized gradient approximation (GGA) with PBE+U does not. In this work, we assess the performance of different exchange-correlation functionals by comparing the meta-GGA functionals SCAN and r2SCAN meta-GGA functionals with HSE06 in predicting CO vibrational frequencies on cerium oxide surfaces. The meta-GGA functionals offer no significant improvement for oxidized CeO2(111) and CeO2(110) surfaces and fail to localize excess charge on the reduced surfaces. Adding a Hubbard U term improves charge localization, but the predicted vibrational frequencies still fall short of HSE06 accuracy. These limitations are attributed to the meta-GGA's inability to adequately capture facet- and configuration-specific donation and back-donation effects, which influence the C-O bond length and CO force constant upon adsorption. Despite the higher computational cost when used with plane wave basis sets, hybrid DFT remains essential for accurate interpretation of experimental results.

This study investigates the novelty of the crystalline and electronic structure of Mg–Ti-doped ZnO and the codoped Zn1−x−yMgxTiyO structures using Gaussian and plane wave basis sets, as implemented in the CP2K code. The goal of incorporating low concentration of Mg and Ti into ZnO is to influence its electronic properties without significantly altering its geometrical and crystalline structure. Within the framework of density functional theory, we analyze various doped and codoped configurations. Our results show that Ti-doped ZnO exhibits an indirect bandgap, while Mg doping preserves the direct semiconductor behavior of the ZnO structure, with an increase in the bandgap energy. Additionally, the codoped Zn1−x−yMgxTiyO system, at varying concentrations of Ti and Mg, displays minimal lattice deformation. These findings suggest that this material could be a promising candidate for transparent electronic devices, highlighting the importance of understanding the electronic structure of ZnO to optimize its physical properties.

Full configuration interaction (FCI) calculations have historically faced significant challenges in dealing with periodic systems. The plane-wave basis sets are valued for their efficiency and broad applicability in various computational physics and chemistry simulations. Because of their natural periodicity, the plane-wave basis sets offer a potential solution to this problem. Moreover, FCI can address the limitations of widely used methods, such as density functional theory (DFT) with plane-wave basis sets, in accurately describing strongly correlated systems. However, the large basis set nature of the plane-wave makes them unsuitable for direct application in FCI calculations. To address this challenge, we propose an improved algorithm based on the correlation-optimized virtual orbital (COVOS) framework. By incorporating rotational matrices to enhance the active space dimension and optimizing orbitals through iterative coupled processes, we successfully compress the extensive plane-wave basis set into a manageable number of virtual orbitals suitable for FCI calculations while retaining most of the original basis set characteristics. We apply this method to supercell calculations and potential energy curves of periodic metallic systems. To further validate our approach, we test it on nonperiodic small molecular systems and compare the results with those obtained from DFT, second-order Møller-Plesset perturbation theory (MP2), random phase approximation (RPA), FCI calculations using the 6-31G or cc-pVDZ basis sets, and the original COVOS algorithm. The improved COVOS framework demonstrates significant advantages in convergence and correlation description over the original method. Furthermore, we observe metal divergence issues in MP2 calculations for certain metallic systems and note that RPA may overestimate the correlation energy of such systems. These findings underscore the importance of achieving FCI calculations with plane-wave basis sets.

A longstanding challenge in materials science has been the computational modeling of interfaces between materials with different lattice parameters. Traditional approaches using plane-wave basis sets require either introducing artificial strain through unified lattice parameters or constructing prohibitively large supercells to accommodate the mismatch. These limitations have often deterred researchers from investigating large, mismatched interfaces, creating a gap in the understanding of these important systems. This work introduces an innovative algorithm that adaptively tunes the plane-wave basis sets to match the periodic structure of each material across the interface. By eliminating the need for extensive supercells or compromised lattice parameters, this new method reduces computational costs while retaining reliable results. The ability to efficiently calculate the eigen-energies of such mismatched systems, a crucial step for full density functional theory (DFT) calculations, is demonstrated with two dimensional versions of InAs/Si and SiC/Si interface potentials.

To meet the demands for functional layers in inverted flexible perovskite solar cells, high-performance formamidinium-based perovskite solar cells, and high-performance photodetectors in future applications, it is crucial to appropriately reduce the bandgap of third-generation wide-bandgap semiconductor materials. In this study, we first optimized doping sites through Ag-Cl and Ag-S configurations to establish stable substitution patterns, followed by density functional theory (DFT) calculations using the Generalized Gradient Approximation with the Perdew-Burke-Ernzerhof (GGA-PBE) functional, implemented in the Vienna Ab initio Simulation Package (VASP). A plane-wave basis set with a cutoff energy of 450 eV and a 3 × 4 × 3 Γ-centered k-mesh were adopted to investigate the effects of Mg-Cl, Mg-S, Zn-Cl, and Zn-S co-doping on the structural stability, electronic properties, and optical characteristics of β-Ga2O3. Based on structural symmetry, six doping sites were considered, with Ag-S/Cl systems revealing preferential occupation at octahedral Ga(1) sites through site formation energy analysis. The results demonstrate that Mg-Cl, Mg-S, Zn-Cl, and Zn-S co-doped systems exhibit thermodynamic stability. The bandgap of pristine β-Ga2O3 was calculated to be 2.08 eV. Notably, Zn-Cl co-doping achieves the lowest bandgap reduction to 1.81 eV. Importantly, all co-doping configurations, including Mg-Cl, Mg-S, Zn-Cl, and Zn-S, effectively reduce the bandgap of β-Ga2O3. Furthermore, the co-doped systems show enhanced visible light absorption (30% increase at 500 nm) and improved optical storage performance compared to the pristine material.

Development of Hessian calculation using the combined plane wave and localized basis sets method and its application to adsorption of a water molecule on Pt(111) surface

Born-Oppenheimer molecular dynamics (BOMD) simulations are of great interest for the dynamic properties of molecular and solid systems. However, BOMD simulations necessitate not only an extensive period of dynamical evolution but also costly self-consistent-field (SCF) electronic structure calculations, especially for hybrid functional-based BOMD (H-BOMD) simulations within plane-wave basis sets. Here, we propose an improved always stable predictor-corrector (ASPC) method for the wave function extrapolation to accelerate the plane-wave H-BOMD simulations, named projected ASPC (PASPC), yielding a wave function closer to the actual solution space and efficiently reducing the number of SCF iterations at each MD step. We investigated the convergence properties of different extrapolation schemes for molecular and solid systems. Numerical results demonstrate that plane-wave H-BOMD simulations can be significantly faster than conventional cases by combining the accelerated algorithms with the PASPC method. The energy drift is also evaluated, showing that PASPC produces energy drift with smaller oscillations and can simulate a larger time step for systems containing heavy atoms, demonstrating the accuracy of the extrapolation schemes. Furthermore, H-BOMD simulations showcase more accurate power and infrared spectra of silicon dioxide and liquid water that are comparable to those of experimental measurements.